5/27/2013 GLOBAL CLIMATE OBSERVING SYSTEM ESSENTIAL CLIMATE VARIABLES (ECV) GEOSS: A DISTRIBUTED SYSTEM OF SYSTEMS GAPS IN BIODIVERSITY MONITORING

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1 GLOBAL CLIMATE OBSERVING SYSTEM ESSENTIAL CLIMATE VARIABLES (ECV) Domain GCOS Essential Climate Variables ESSENTIAL BIODIVERSITY VARIABLES (EBV) FROM IMAGE ANDREW K. SKIDMORE ITC, UNIVERSITY OF TWENTE. 5+ GCOS Essential Climate Variables (ECVs) (21) Land cover, fapar, LAI, biomass, (fire) disturbance, soil moisture, soil carbon Atmospheric (over land, sea and ice) Oceanic Surface:[1] Air temperature, Wind speed and direction, Water vapour, Pressure, Precipitation, Surface radiation budget. Upper-air:[2] Temperature, Wind speed and direction, Water vapour, Cloud properties, Earth radiation budget (including solar irradiance). Composition: Carbon dioxide, Methane, and other long-lived greenhouse gases[3], Ozone and Aerosol, supported by their precursors[4]. Surface:[5] Sea-surface temperature, Sea-surface salinity, Sea level, Sea state, Sea ice, Surface current, Ocean colour, Carbon dioxide partial pressure, Ocean acidity, Phytoplankton. Sub-surface: Temperature, Salinity, Current, Nutrients, Carbon dioxide partial pressure, Ocean acidity, Oxygen, Tracers. Terrestrial River discharge, Water use, Groundwater, Lakes, Snow cover, Glaciers and ice caps, Ice sheets, Permafrost, Albedo, Land cover (including vegetation type), Fraction of absorbed photosynthetically active radiation (FAPAR), Leaf area index (LAI), Above-ground biomass, Soil carbon, Fire disturbance, Soil moisture. 2 GEOSS: A DISTRIBUTED SYSTEM OF SYSTEMS Improve coordination of observation systems GMES Space component is the EU contribution to GEOSS. Link all platforms: in-situ, aircraft, satellite, buoys, etc. Exchange data and information Improve decision-makers abilities GAPS IN BIODIVERSITY MONITORING The Living Planet Database and Index (WWF) holds time-series data for over 11, populations of more than 27 vertebrate species from around the world. Systematic stratified designed to address bias within the data set Societal Benefit Areas 3 4/5 ESSENTIAL BIODIVERSITY VARIABLES PROPOSED BY GEOBON AND ACCEPTED BY CBD FRAMEWORK BASED ON PRESSURES-STATE-RESPONSE-IMPACT FRAMEWORK Characteristics of EBV s Ability to detect change Quantifiable Repeatable Transferable Biological Biodiversity dimensions Genetic diversity Species distributions Ecosystems (functioning &services)

2 Which may not be measured with IS or EO Allelic richness Fst Phylogenic diversity Gene diversity Functional attributes (diet, breeding system, body mass) Co-ancestry key traits Phylogenic diversity Turnover (beta-diversity) Degree of protection Use rate by humans Use benefits to humans (economic, spiritual, cultural ) Non-use benefits (existence, aesthetic ) 7 8 LAND COVER TYPE, VEGETATION TYPE, PLANT FUNCTIONAL GROUPS PFT use structural, physiological and or phenological features to group species into plant communities (Ustin et al. 28) PFT are a mix of traits for example, growth form, plant height and/or species groups: Circumpolar Arctic Vegetation B barrens G graminoid-dominated tundras P prostrate-shrub-dominated tundras S erect-shrub-dominated tundras W wetlands ARCTIC PLANT FUNCTIONAL TYPE CONTRADICTIONS Arctic vegetation has been characterized as discrete patches as well as continuous (Shaver et al., 27). Do types actually exist Are there universal classification schemes. Bret-Harte et al (28) could not predict plant functional traits (biomass, N) per PFTypes LAND COVER TYPE, VEGETATION TYPE, PLANT FUNCTIONAL GROUPS PFTypes derived from air photo interpretation and supervised classification Plant Functional Types are pre-classified define e strata transferability issues FUNCTIONAL CONVERGENCE ECOLOGICAL CONTEXT Functional convergence morphological and phenological traits should be linked to underlying physiological traits e.g. biochemical, leaf and canopy structure for photosynthesis Structural responses include root to shoot ratios specific leaf area (SLA = area/dry weight) leaf orientation (Field & Mooney, 1986). Leads to concept of plant functional traits

3 PLANT FUNCTIONAL TRAITS Plant functional traits may be measured from EO/IS or in situ LAI Cornelissen, J. H. C. et al. (23) Biomass fapar Specific leaf area From plant functional traits derive essential biodiversity variables and plant functional types Cover type Leaf life span (Reich et al. 1992) AT WHICH SCALE CAN PLANT FUNCTIONAL TRAITS BE MEASURED? 15 Cell Leaf Canopy Ecosystem/Landscape Global Leaf Canopy Landscape/aircraft Continental/satellite Are traits transferable between scales? 16 ACQUIRING HYPERSPECTRAL DATA AT DIFFERENT SCALES LABORATORY/FIELD LEVEL AIRBORNE/SATELLITE LEVEL Field spectrometers Controlled experiments: Vegetation foliar biochemical response to fetilizer and salinity Spectral bands sensitive s e foliar chemicals Sufficient samples Imaging spectrometers Short dwell time: sufficient SNR Upscaling Field samples (leaf and soil) Analyze foliar and soil chemical by wet chemistry

4 Significant difference between Croton and Themeda Significant difference between Themeda and Croton [image] Significant Themeda Croton Significant Themeda Croton 13 3 Difference of Themeda - Croton [Lab Reflectance] Difference of Themeda - Croton [Image Reflectance] /27/213 SPECIES : SCALING FROM LEAF AND LANDSCAPE/AIRCRAFT LEVEL SPECIES AIRCRAFT LEVEL HYMAP + LIDAR - ANN CROTON DYCHOGAMUS AND THEMADA TRIANDRA AT 1 M AND AT 3 M Reflectance [%] Difference between the 2 reflectance curves Field reflectance 27 salt marsh species could be discriminated based on their spectra Mapped HyMAP + LIDAR using ANN 8% map accuracy API 3% map accuracy Refelctance [value] Aircraft reflectance Schmidt and Skidmore 21, IJRS 19 Schmidt and Skidmore 22 2 MAPPING SPECIES (AS A TRAIT) IS NOT MAPPING PLANT FUNCTIONAL TYPE OR LAND COVER CLASS Scaling in space as well as by theme EMPIRICAL MODELS, RADIATIVE TRANSFER MODELS, BOTH? PLANT FUNCTIONAL TRAITS/EBV FROM IS Empirical models Bands VI Red edge features Radiative Transfer Models Cell Leaf (PROSPECT) Canopy (SAIL) Combined (PROSAIL, SLC) PROSPECT LEAF RADIATIVE TRANSFER MODEL PROSPECT (Jacquemoud & Baret 199) Virtual leaf model simulates the reflectance and transmittance of a single leaf PROSPECT input parameters based on: Transferability for both are an issue

5 PROSAIL RADIATIVE TRANSFER MODEL PROSAIL RTM INVERSION PLANT FUNCTIONAL TRAITS FROM IMAGE SAIL model: Scattering by arbitrary inclined leaves (Verhoef 1984) PROSAIL LUT for inversion Pre-compute a large number of cases from the parameter range and create a LUT of parameters versus wavelengths Measure a spectrum and find from the inverted LUT the closest match to the measured wavelengths (or invert with an ANN) Parameters of soil-leaf-canopy models (SLC) LEAF AREA INDEX (LAI) - HETEROGENOUS GRASSLAND USING PROSAIL INVERSION - FIELD LEVEL LEAF AREA INDEX (LAI) - HETEROGENOUS GRASSLAND USING PROSAIL INVERSION SATELLITE LEVEL 1 species 2 species 3 species 4 species R 2 =.81 R 2 =.69 R 2 =.55 R 2 =.37 RMSE=.76 RMSE=1.1 RMSE=1.6 RMSE=1.5 n=32 n=75 n=59 n=19 Multi-biome (MB) Bacour et al. (26) Standard ESA MERIS CCC product BEAM Retrieves biophysical parameters using a ANN 46,656 simulations Single Biome (SB) Si et al 212 As developed by Darvishzadeh et al 28 Retrieves biophysical parameters using a LUT 1, simulations Darvishzadeh et al. 28 Both Methods: 1) PROSAIL RTM generates fapar, fcover, LAI, and LAI Cab from 11MERIS bands and the 3 angles defining the observation geometry 2) Both techniques validated with n = 47 samples 3) To select the optimal spectra corresponding to a given measurement, the RMSE between measured and modeled spectra calculated 4) LCC calculated from the SPAD. CCC calcluated from LCC*LAI PLANT FUNCTIONAL TRAITS FROM IMAGE LEAF AREA INDEX (LAI) - HETEROGENOUS GRASSLAND USING PROSAIL INVERSION SATELLITE LEVEL Specificity to local conditions to constrain the parameter range LUT configurations must trade-off general and local parameterizations Different retrieval techniques Multiple species Yali et al. 212 LAI SB LAI MB R RMSE

6 CONCENTRATION OF FOLIAR BIOCHEMICALS - NITROGEN CYCLE PLANT FUNCTIONAL TRAITS FROM IMAGE NITROGEN ESTIMATION MIXED SPECIES RED EDGE POSITION - CANOPY LEVEL Leaf N correlates with leaf chlorophyll (Daughtry et al 2) Increased N supply enhances dry matter and protein Above ground N strongly related to the red edge index At higher concentrations of N, red edge shifts to longer wavelengths x = rye = maize = mixed grass/herb 31 Cho & Skidmore 26: RSE 11: CANOPY N AND TOTAL POLYPHENOLS AIRCRAFT/LANDSCAPE LEVEL Geology Hymap image over Kruger National park Binary image slice trees from grass Lab analysis: Foliar N and total polyphenol (tannin) linked to specific wavelengths in the visible and NIR as well as red edge Foliar nitrogen: 583, 679, 71, 291, 2128, 2146 nm (Mutanga et al 24) Foliar polyphenol: 71, 84, 1641, 1714, 1738, 2146 and 2182 nm (Ferwerda et al 26) Neural network modeled key bands with foliar N and total polyphenol Inverted neural network 33 Foliar nitrogen grasses Foliar nitrogen mopane Tannin mopane Skidmore et al RSE. (21) 34 AND SO TO RECAP versus TRAITS LINKED TO EBV DISTINCT CLASS versus CONTINUOUS VARIABLES FUNCTIONAL CONVERGENCE DERIVING ESSENTIAL BIODIVERSITY VARIABLES SPECIES BIOMASS LAI FOLIAR BIOCHEMICALS (N, polyphenols, P, Na, Ca. Mg) EMPIRICAL versus RADIATIVE TRANSFER MODELS

7 EMPIRICAL MODELS OR RADIATIVE TRANSFER MODELS? Challenges Inductive model Data driven Missing parameters Targeted range Validation Site and sensor specific Advantages Data based Specific parameterization Challenges Deductive (hybrid) model Requires parameterization Missing parameters (polyphenols) Wide range Calibration (input data) Simple systems only Advantages Physically based Generic parameterization ADD DATA DRIVEN MODELS TO RTM SO-CALLED DATA ASSIMILATION Transferability for both APPROACHES are an issue Cover (LAI), height, clumping Leaf Nitrogen retention Secondary production Cover (LAI), height, clumping Leaf Nitrogen retention Secondary production 39 7